





'''simple docstring'''






import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
					from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_=13		,   a_=30		,   a_=2		,   a_=3		,   a_=True		,   a_=True		,   a_=32		,   a_=5		,   a_=4		,   a_=37		,   a_="gelu"		,   a_=0.1		,   a_=0.1		,   a_=10		,   a_=0.02		,   a_=None		,   a_=2		,   ):
			lowerCAmelCase  :     str       						= parent
			lowerCAmelCase  :     int       						= batch_size
			lowerCAmelCase  :     Optional[Any]       						= image_size
			lowerCAmelCase  :     str       						= patch_size
			lowerCAmelCase  :     str       						= num_channels
			lowerCAmelCase  :     int       						= is_training
			lowerCAmelCase  :     Optional[int]       						= use_labels
			lowerCAmelCase  :     Optional[int]       						= hidden_size
			lowerCAmelCase  :     int       						= num_hidden_layers
			lowerCAmelCase  :     Dict       						= num_attention_heads
			lowerCAmelCase  :     int       						= intermediate_size
			lowerCAmelCase  :     List[str]       						= hidden_act
			lowerCAmelCase  :     Any       						= hidden_dropout_prob
			lowerCAmelCase  :     Tuple       						= attention_probs_dropout_prob
			lowerCAmelCase  :     int       						= type_sequence_label_size
			lowerCAmelCase  :     List[Any]       						= initializer_range
			lowerCAmelCase  :     Optional[int]       						= scope
			lowerCAmelCase  :     Tuple       						= encoder_stride

			# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
			lowerCAmelCase  :     int       						= (image_size // patch_size) ** 2
			lowerCAmelCase  :     Optional[Any]       						= num_patches + 1



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[Any]       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

			lowerCAmelCase  :     str       						= None
			if self.use_labels:
					lowerCAmelCase  :     Union[str, Any]       						= ids_tensor([self.batch_size]		,   self.type_sequence_label_size						)

			lowerCAmelCase  :     Tuple       						= self.get_config()

			return config, pixel_values, labels



	def 	_lowerCamelCase     ( self						):
			return ViTConfig(
			    image_size=self.image_size		,   patch_size=self.patch_size		,   num_channels=self.num_channels		,   hidden_size=self.hidden_size		,   num_hidden_layers=self.num_hidden_layers		,   num_attention_heads=self.num_attention_heads		,   intermediate_size=self.intermediate_size		,   hidden_act=self.hidden_act		,   hidden_dropout_prob=self.hidden_dropout_prob		,   attention_probs_dropout_prob=self.attention_probs_dropout_prob		,   is_decoder=a_		,   initializer_range=self.initializer_range		,   encoder_stride=self.encoder_stride		,   )



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     List[str]       						= ViTModel(config=a_						)
			model.to(a_						)
			model.eval()
			lowerCAmelCase  :     Any       						= model(a_						)
			self.parent.assertEqual(result.last_hidden_state.shape		,   (self.batch_size, self.seq_length, self.hidden_size)						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= ViTForMaskedImageModeling(config=a_						)
			model.to(a_						)
			model.eval()
			lowerCAmelCase  :     Union[str, Any]       						= model(a_						)
			self.parent.assertEqual(
			    result.reconstruction.shape		,   (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

			# test greyscale images
			lowerCAmelCase  :     List[Any]       						= 1
			lowerCAmelCase  :     int       						= ViTForMaskedImageModeling(a_						)
			model.to(a_						)
			model.eval()

			lowerCAmelCase  :     int       						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
			lowerCAmelCase  :     Union[str, Any]       						= model(a_						)
			self.parent.assertEqual(result.reconstruction.shape		,   (self.batch_size, 1, self.image_size, self.image_size)						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     Any       						= self.type_sequence_label_size
			lowerCAmelCase  :     List[Any]       						= ViTForImageClassification(a_						)
			model.to(a_						)
			model.eval()
			lowerCAmelCase  :     str       						= model(a_		,   labels=a_						)
			self.parent.assertEqual(result.logits.shape		,   (self.batch_size, self.type_sequence_label_size)						)

			# test greyscale images
			lowerCAmelCase  :     List[str]       						= 1
			lowerCAmelCase  :     str       						= ViTForImageClassification(a_						)
			model.to(a_						)
			model.eval()

			lowerCAmelCase  :     int       						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
			lowerCAmelCase  :     str       						= model(a_						)
			self.parent.assertEqual(result.logits.shape		,   (self.batch_size, self.type_sequence_label_size)						)





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= self.prepare_config_and_inputs()
			(
			    (
			    lowerCAmelCase
			)						,		(
			    lowerCAmelCase
			)						,		(
			    lowerCAmelCase
			)						,		
			)  :     str       						= config_and_inputs
			lowerCAmelCase  :     Dict       						= {"pixel_values": pixel_values}
			return config, inputs_dict






@require_torch
class 		lowerCamelCase	(   _A   , _A   , unittest.TestCase							):
	snake_case_   = (
	    (
	        ViTModel,
	        ViTForImageClassification,
	        ViTForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	snake_case_   = (
	    {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
	    if is_torch_available()
	    else {}
	)
	snake_case_   = True

	snake_case_   = False
	snake_case_   = False
	snake_case_   = False



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     str       						= ViTModelTester(self						)
			lowerCAmelCase  :     Dict       						= ConfigTester(self		,   config_class=a_		,   has_text_modality=a_		,   hidden_size=37						)



	def 	_lowerCamelCase     ( self						):
			self.config_tester.run_common_tests()



	@unittest.skip(reason="ViT does not use inputs_embeds"						)
	def 	_lowerCamelCase     ( self						):
			pass



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Dict       						= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCAmelCase  :     Any       						= model_class(a_						)
					self.assertIsInstance(model.get_input_embeddings()		,   (nn.Module)						)
					lowerCAmelCase  :     Optional[Any]       						= model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(a_		,   nn.Linear						)						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Union[str, Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCAmelCase  :     List[str]       						= model_class(a_						)
					lowerCAmelCase  :     int       						= inspect.signature(model.forward						)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					lowerCAmelCase  :     Any       						= [*signature.parameters.keys()]

					lowerCAmelCase  :     List[str]       						= ["pixel_values"]
					self.assertListEqual(arg_names[:1]		,   a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Any       						= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Any       						= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_image_modeling(*a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*a_						)





	@slow
	def 	_lowerCamelCase     ( self						):
			for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCAmelCase  :     Union[str, Any]       						= ViTModel.from_pretrained(a_						)
					self.assertIsNotNone(a_						)






def   __A    (				):
		lowerCAmelCase  :     Optional[int]       						= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
		return image

@require_torch
@require_vision
class 		lowerCamelCase	(   unittest.TestCase							):



	@cached_property
	def 	_lowerCamelCase     ( self						):
			return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"						) if is_vision_available() else None



	@slow
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= ViTForImageClassification.from_pretrained("google/vit-base-patch16-224"						).to(a_						)

			lowerCAmelCase  :     Tuple       						= self.default_image_processor
			lowerCAmelCase  :     List[str]       						= prepare_img()
			lowerCAmelCase  :     List[str]       						= image_processor(images=a_		,   return_tensors="pt"						).to(a_						)

			# forward pass
			with torch.no_grad():
					lowerCAmelCase  :     int       						= model(**a_						)

			# verify the logits
			lowerCAmelCase  :     List[str]       						= torch.Size((1, 1_000)						)
			self.assertEqual(outputs.logits.shape		,   a_						)

			lowerCAmelCase  :     Any       						= torch.tensor([-0.2744, 0.8215, -0.0836]						).to(a_						)

			self.assertTrue(torch.allclose(outputs.logits[0, :3]		,   a_		,   atol=1e-4						)						)



	@slow
	def 	_lowerCamelCase     ( self						):
			# ViT models have an `interpolate_pos_encoding` argument in their forward method,
			# allowing to interpolate the pre-trained position embeddings in order to use
			# the model on higher resolutions. The DINO model by Facebook AI leverages this
			# to visualize self-attention on higher resolution images.
			lowerCAmelCase  :     Optional[Any]       						= ViTModel.from_pretrained("facebook/dino-vits8"						).to(a_						)

			lowerCAmelCase  :     int       						= ViTImageProcessor.from_pretrained("facebook/dino-vits8"		,   size=480						)
			lowerCAmelCase  :     Union[str, Any]       						= prepare_img()
			lowerCAmelCase  :     List[Any]       						= image_processor(images=a_		,   return_tensors="pt"						)
			lowerCAmelCase  :     List[Any]       						= inputs.pixel_values.to(a_						)

			# forward pass
			with torch.no_grad():
					lowerCAmelCase  :     Tuple       						= model(a_		,   interpolate_pos_encoding=a_						)

			# verify the logits
			lowerCAmelCase  :     Optional[int]       						= torch.Size((1, 3_601, 384)						)
			self.assertEqual(outputs.last_hidden_state.shape		,   a_						)

			lowerCAmelCase  :     List[Any]       						= torch.tensor(
			    [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]						).to(a_						)

			self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]		,   a_		,   atol=1e-4						)						)





	@slow
	@require_accelerate
	@require_torch_gpu
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Dict       						= ViTModel.from_pretrained("facebook/dino-vits8"		,   torch_dtype=torch.floataa		,   device_map="auto"						)
			lowerCAmelCase  :     Union[str, Any]       						= self.default_image_processor

			lowerCAmelCase  :     Tuple       						= prepare_img()
			lowerCAmelCase  :     Any       						= image_processor(images=a_		,   return_tensors="pt"						)
			lowerCAmelCase  :     Optional[Any]       						= inputs.pixel_values.to(a_						)

			# forward pass to make sure inference works in fp16
			with torch.no_grad():
					lowerCAmelCase  :     Dict       						= model(a_						)










'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   {
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class 		lowerCamelCase	(   _A							):
	snake_case_   = "gpt_neox"



	def __init__( self		,   a_=50_432		,   a_=6_144		,   a_=44		,   a_=64		,   a_=24_576		,   a_="gelu"		,   a_=0.25		,   a_=10_000		,   a_=0.0		,   a_=0.0		,   a_=0.1		,   a_=2_048		,   a_=0.02		,   a_=1e-5		,   a_=True		,   a_=0		,   a_=2		,   a_=False		,   a_=True		,   a_=None		,   **a_		,   ):
			super().__init__(bos_token_id=a_		,   eos_token_id=a_		,   **a_						)
			lowerCAmelCase  :     Dict       						= vocab_size
			lowerCAmelCase  :     str       						= max_position_embeddings
			lowerCAmelCase  :     int       						= hidden_size
			lowerCAmelCase  :     Optional[Any]       						= num_hidden_layers
			lowerCAmelCase  :     int       						= num_attention_heads
			lowerCAmelCase  :     Any       						= intermediate_size
			lowerCAmelCase  :     Tuple       						= hidden_act
			lowerCAmelCase  :     Any       						= rotary_pct
			lowerCAmelCase  :     str       						= rotary_emb_base
			lowerCAmelCase  :     Tuple       						= attention_dropout
			lowerCAmelCase  :     Optional[int]       						= hidden_dropout
			lowerCAmelCase  :     int       						= classifier_dropout
			lowerCAmelCase  :     str       						= initializer_range
			lowerCAmelCase  :     str       						= layer_norm_eps
			lowerCAmelCase  :     List[Any]       						= use_cache
			lowerCAmelCase  :     Dict       						= tie_word_embeddings
			lowerCAmelCase  :     Any       						= use_parallel_residual
			lowerCAmelCase  :     Union[str, Any]       						= rope_scaling
			self._rope_scaling_validation()

			if self.hidden_size % self.num_attention_heads != 0:
					raise ValueError(
					    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"						)





	def 	_lowerCamelCase     ( self						):
			if self.rope_scaling is None:
					return

			if not isinstance(self.rope_scaling		,   a_						) or len(self.rope_scaling						) != 2:
					raise ValueError(
					    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
					    F'''got {self.rope_scaling}'''						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("type"		,   a_						)
			lowerCAmelCase  :     int       						= self.rope_scaling.get("factor"		,   a_						)
			if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
					raise ValueError(
					    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''						)
			if rope_scaling_factor is None or not isinstance(a_		,   a_						) or rope_scaling_factor <= 1.0:
					raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''						)



